Mini-Project 2
Kimberly Zhang, Emily Raphael, Maddie Haines
October 31, 2017
#loading data
load("house_elections.rda")
load("candidates.rda")
load("committees.rda")
load("contributions.rda")
library(tidyverse)
require(scales)
library(ggthemes)#filtered candidates
candidates_filtered <- candidates %>%
select(-c(cand_st1, cand_st2, cand_city, cand_state,cand_zip))
#filtered contributions
contributions_filtered <- contributions %>%
select(cmte_id, name, city, state, zip_code, transaction_dt, transaction_amt, cand_id, other_id)
#filter committees
committees_filtered <- committees %>%
select(cmte_id, cmte_name, cmte_city, cmte_state, cmte_zip, cmte_party_affiliation, cand_id)#joining the filtered tables
complete_table <- left_join(candidates_filtered, contributions_filtered, by = "cand_id")#creating list of state names for function
c <- complete_table %>%
select(cand_office_state, cand_election_yr, cand_office_state) %>%
distinct(cand_office_state)
kimtest <- complete_table %>%
filter(cand_election_yr == "2012", cand_office == "S")
#Final list
state_names <- distinct(kimtest, cand_office_state) %>%
.$cand_office_state#lubridate
require(lubridate)
#changes int dates into actual dates
dates <- complete_table %>%
mutate(date = mdy(transaction_dt))# sorting the complete table, only interested in Senate, 2012 races in the timeline of the election year
filtered_names <- dates %>%
filter(cand_election_yr == "2012", cand_office == "S", date >= "2012-01-01")%>%
group_by(cand_name, transaction_dt) %>%
mutate(total = sum(transaction_amt))## [[1]]
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blog post We used data from the Federal Election Commission